practice a little every day, paws crossed ♡
Data Scientist Interview Questions
Practice 9403 coding, system design and machine learning questions from 546 companies, sorted by role, topic and interview round. Every question has step-by-step hints, a code runner that supports 8 languages and an AI mentor; members also get the full solution walkthrough.
- 9403
- Questions
- 546
- Companies
- 11
- Topics
- 8
- Languages
Showing 631–660 of 1971 results
- DoorDashHardData ScientistDiagnose and experiment to reduce late deliveries
This question evaluates a data scientist's competencies in causal inference, metrics engineering, data instrumentation and joining, root‑cause analysis, and experiment design focused on operational delivery performance.
Analytics & ExperimentationTechnical Screen - DoorDashHardData ScientistDiagnose and reduce cold-food refund costs
This problem tests the ability to diagnose root causes of operational cost drivers and design a rigorous experiment to validate potential mitigation policies.
Analytics & ExperimentationTechnical ScreenCausal InferenceExperimentationMarketplace Analytics - DoorDashHardData ScientistDiagnose cold-food spike and design experiments
This question evaluates a data scientist's skills in product analytics, metric design, diagnostics, causal inference, and experimental design for diagnosing cold-food complaint spikes using logs, GPS, weather and complaint labels.
Analytics & ExperimentationOnsite - DoorDashMediumData ScientistDiagnose completed orders drop in Los Angeles
This question evaluates a data scientist's competency in product analytics, causal inference, funnel and metric decomposition, segmentation, and experimentation for marketplace platforms, focusing on diagnosing sustained drops in completed orders.
Analytics & ExperimentationOnsite - DoorDashHardData ScientistEvaluate Account-Partner Onboarding with Success Metrics
This interview question evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer for Evaluate Account-Partner Onboarding with Success Metrics states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Analytics & ExperimentationOnsite - DoorDashHardData ScientistEvaluate Biker Feature Success
This question evaluates experimentation and product analytics skills—metric design, causal inference, and marketplace impact assessment—in the context of a two-sided delivery marketplace, with attention to eligibility, routing, and matching effects.
Analytics & ExperimentationOnsite - DoorDashMediumData ScientistEvaluate Courier-Selected Delivery Distance Limits
This question tests your ability to design an A/B test for a marketplace feature while accounting for network effects, trade-offs between supply-side control and demand-side fulfillment, and selecting appropriate metrics.
Analytics & ExperimentationOnsiteA/B TestingMarketplace AnalyticsCausal Inference - DoorDashHardData ScientistEvaluate Dasher Initiatives with A/B Testing and Metrics
This interview question evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer for Evaluate Dasher Initiatives with A/B Testing and Metrics states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Analytics & ExperimentationOnsite - DoorDashMediumData ScientistEvaluate Impact of $1 Fee on Fast-Food Profitability
This interview question evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer for Evaluate Impact of $1 Fee on Fast-Food Profitability states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Analytics & ExperimentationOnsite - DoorDashHardData ScientistEvaluate Top-Dasher Program's Benefits and Challenges
Evaluate Top-Dasher Program's Benefits and Challenges evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Analytics & ExperimentationOnsite - DoorDashMediumData ScientistEvaluate a Bike Dasher Program with a Controlled Experiment
Tests marketplace experimentation design, tackling spatial-temporal spillover effects, metric selection, and decision criteria for a multimodal courier fleet.
Analytics & ExperimentationTechnical ScreenA/B TestingMarketplace ExperimentationCausal Inference - DoorDashMediumData ScientistEvaluate a Bike-Delivery Pilot with Marketplace Metrics
This problem tests the ability to design an A/B test for a logistics marketplace, focusing on metric definition, experiment randomization strategy, and identifying potential confounding variables.
Analytics & ExperimentationTechnical ScreenA/B TestingMarketplaceLogistics - DoorDashHardData ScientistEvaluate a new ranking model
This question evaluates expertise in experimentation design and causal inference within two-sided marketplace environments. It tests the ability to handle interference, SUTVA violations, metric selection, and rollout safety when deploying ranking model upgrades — core competencies for data scientist roles focused on product analytics and A/B testing.
Analytics & ExperimentationOnsite - DoorDashHardData ScientistExperiment on increasing order notifications
This question evaluates a data scientist's skills in experimental design, causal inference, metric definition and aggregation, power/MDE calculation, and cross‑channel attribution for order‑related messaging campaigns.
Analytics & ExperimentationOnsite - DoorDashHardData ScientistExplain interest and influence stakeholders
This question evaluates stakeholder management, cross-functional influence, data-driven decision-making, ownership, and the ability to reason about multi-sided marketplace dynamics for a Data Scientist role.
Behavioral & LeadershipOnsite - DoorDashEasyData ScientistFind Monthly Top Customers Excluding High-Frequency Users
This problem tests proficiency in window functions, filtering based on aggregated metrics, and handling ranking logic in SQL.
Data Manipulation (SQL/Python)Technical ScreenSQLWindow FunctionsData Aggregation - DoorDashMediumData ScientistGenerate Weekly Revenue and Engagement Summary with Pandas
This question evaluates proficiency in data manipulation and analytics with Pandas and SQL, covering aggregations, distinct purchase counts, lambda-based user-tier classification, and platform-level conversion-rate computation.
Data Manipulation (SQL/Python)Technical Screen - DoorDashMediumData ScientistHow would you mentor junior teammates?
A DoorDash Data Scientist onsite behavioral & leadership question that asks how you mentor others as a senior IC — especially more junior teammates. Strong answers diagnose each person's gaps, tailor support across levels and cross-functional partners, use concrete mechanisms (ramp plans, feedback rubrics, scalable templates, sponsorship), mentor through influence rather than authority, and measure effectiveness via independence and impact, all anchored in a detailed STAR example.
Behavioral & LeadershipOnsite - DoorDashEasyData ScientistHow would you test a bike delivery option?
This problem tests the ability to design an A/B test, define success metrics, and account for confounding variables when introducing a new logistics channel (bike delivery) to a marketplace.
Analytics & ExperimentationTechnical ScreenA/B TestingMetricsMarketplace Analytics - DoorDashMediumData ScientistIdentify Challenges and Solutions for Bike-Delivery Program
Evaluates operational and strategic thinking for a bike-delivery courier program. Strong answers identify marketplace, safety, weather, regulatory, courier, merchant, customer-experience, and unit-economics risks, then pair each challenge with practical mitigations and measurable rollout guardrails.
Behavioral & LeadershipTechnical Screen - DoorDashHardData ScientistIdentify Key Drivers of Delivery Decline in Los Angeles
This interview question evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer for Identify Key Drivers of Delivery Decline in Los Angeles states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Analytics & ExperimentationTechnical Screen - DoorDashMediumData ScientistInvestigate Causes of Cold Food Deliveries and Solutions
This interview question evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer for Investigate Causes of Cold Food Deliveries and Solutions states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Analytics & ExperimentationTechnical Screen - DoorDashMediumData ScientistInvestigate Causes of Cold Meal Deliveries
DoorDash data scientist case on cold meal deliveries, covering lifecycle timestamps, complaint metrics, segmentation, root-cause analysis, dispatch and packaging fixes, A/B testing, and marketplace guardrails.
Analytics & ExperimentationTechnical Screen - DoorDashMediumData ScientistInvestigate Causes of Increased Driver Wait Time
This interview question evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer for Investigate Causes of Increased Driver Wait Time states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Analytics & ExperimentationTechnical Screen - DoorDashHardData ScientistInvestigate Declining Successful Orders
This question tests your ability to decompose a high-level drop in a key business metric into granular segments and formulate testable hypotheses to explain the underlying root causes.
Analytics & ExperimentationTechnical ScreenProduct AnalyticsFunnel AnalysisHypothesis Testing - DoorDashHardData ScientistInvestigate Falling Successful Orders
This question evaluates a data scientist's ability to diagnose a drop in a product metric through anomaly verification, data validity checks, funnel and cohort analysis, causal attribution across marketplace sides (customer, restaurant, delivery), and experiment design.
Analytics & ExperimentationTechnical Screen - DoorDashHardData ScientistInvestigate Falling Successful Orders in LA
This problem tests your ability to decompose a drop in a high-level business metric into granular segments, formulate testable hypotheses, and propose methodologies to identify the root cause.
Analytics & ExperimentationTechnical ScreenRoot Cause AnalysisFunnel AnalyticsMarketplace Dynamics - DoorDashMediumData ScientistInvestigate LA Order Drop
This question evaluates marketplace analytics, causal inference, hypothesis-driven investigation, funnel decomposition, and experimentation design skills in a data science context.
Analytics & ExperimentationTechnical Screen - DoorDashMediumData ScientistInvestigate Pop-up Impact on Partner Referral Conversions
This interview question evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer for Investigate Pop-up Impact on Partner Referral Conversions states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Analytics & ExperimentationOnsite - DoorDashMediumData ScientistMeasure Impact of Merchant Variety on Consumer Experience
This interview question evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer for Measure Impact of Merchant Variety on Consumer Experience states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Analytics & ExperimentationOnsite
DoorDash